Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 60006

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 65788

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-15T21:31:05+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

COVID-19 対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	120353
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	118602	0
dateAnnounced	0	1.00	10	10	0	292	0
gender	22741	0.81	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	116312	0.03	8	23	0	8	0
notes	62752	0.48	1	270	0	54725	1
mhlwPatientNumber	119904	0.00	1	11	0	434	0
prefecturePatientNumber	19263	0.84	5	20	0	101081	0
prefectureSourceURL	89004	0.26	5	224	0	3450	0
residence	30737	0.74	1	38	0	1427	0
sourceURL	637	0.99	1	239	0	8486	0
relatedPatients	109266	0.09	2	259	0	6675	0
knownCluster	117852	0.02	3	88	0	231	0
detectedCityTown	93347	0.22	2	22	0	667	0
cityPrefectureNumber	93611	0.22	1	34	0	26733	2
citySourceURL	108417	0.10	9	317	0	3661	0
deceasedDate	118471	0.02	10	10	0	242	0
deceasedReportedDate	119133	0.01	10	62	0	207	0
deathSourceURL	119278	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 118601, FAL: 1752
charterFlightPassenger	120339	0	1.00	TRU: 14
cruisePassengerDisembarked	120342	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	30.59	24.16	-1	10	30	50	100	▆▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	117239
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	117239	0
knownCluster	114788	0.02	3	88	0	228	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-15	2020-08-20	289
deceasedDate	116861	0	2020-02-13	2020-10-17	2020-05-08	149
deceasedReportedDate	116910	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	21211	0.82	FALSE	2	M: 53797, F: 42231
patientStatus	114729	0.02	FALSE	8	Hos: 1246, Dec: 370, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	20: 26141, -1: 21297, 30: 16661, 40: 13874
pcode	0	1.00	FALSE	47	13: 34789, 27: 15421, 14: 10173, 23: 7712
pref	0	1.00	FALSE	47	東京都: 34789, 大阪府: 15421, 神奈川: 10173, 愛知県: 7712
region	0	1.00	FALSE	8	関東地: 60006, 近畿地: 24143, 中部地: 11902, 九州地: 11653
広域圏	9227	0.92	FALSE	8	首都圏: 60289, 近畿圏: 23524, 中部圏: 10482, 九州圏: 7909
通俗的区分	0	1.00	FALSE	11	関東: 60006, 関西: 23524, 東海: 10022, 九州: 7909
fct_pref	0	1.00	FALSE	47	Tok: 34789, Osa: 15421, Kan: 10173, Aic: 7712

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 117239
charterFlightPassenger	117232	0	1.00	TRU: 7
cruisePassengerDisembarked	117228	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 114788, TRU: 2451

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.93）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（0.93）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

　

地方別

陽性者数（単日）

　

移動平均

　

累積

　

単日＋累積

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 231.3935 221.8062 234.4630 251.4183 253.9082 259.5845 255.1005 266.3694
##  [9] 266.0630 275.0557 283.3153 287.6594 287.4471 289.8876
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 220.8298 215.2377
## 44.85714 209.6793 203.2598
## 45.00000 219.0924 210.9557
## 45.14286 234.3095 225.2526
## 45.28571 234.4777 224.1918
## 45.42857 238.3843 227.1616
## 45.57143 231.9205 219.6498
## 45.71429 240.1249 226.2319
## 45.85714 237.4187 222.2553
## 46.00000 243.7800 227.2236
## 46.14286 249.6864 231.8843
## 46.28571 251.5996 232.5106
## 46.42857 249.0813 228.7717
## 46.57143 249.1934 227.6511
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 241.9573 247.5494
## 44.85714 233.9330 240.3525
## 45.00000 249.8336 257.9703
## 45.14286 268.5272 277.5841
## 45.28571 273.3388 283.6247
## 45.42857 280.7848 292.0075
## 45.57143 278.2804 290.5512
## 45.71429 292.6139 306.5069
## 45.85714 294.7072 309.8706
## 46.00000 306.3314 322.8878
## 46.14286 316.9443 334.7464
## 46.28571 323.7193 342.8082
## 46.42857 325.8128 346.1224
## 46.57143 330.5819 352.1241
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 16.86286 25.69346 29.42149 32.21412 30.98707 30.05086 28.04389 27.44826
##  [9] 26.83591 27.22162 27.37673 27.84380 27.91558 28.05971
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 10.04245  6.431945
## 44.85714 18.47886 14.659686
## 45.00000 22.06626 18.172640
## 45.14286 24.85865 20.964896
## 45.28571 23.57549 19.652038
## 45.42857 22.56161 18.597032
## 45.57143 20.25316 16.128990
## 45.71429 19.35279 15.067311
## 45.85714 18.34245 13.846279
## 46.00000 18.42839 13.773536
## 46.14286 18.29041 13.480411
## 46.28571 18.53493 13.607117
## 46.42857 18.38300 13.336762
## 46.57143 18.32878 13.177539
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 23.68327 27.29377
## 44.85714 32.90806 36.72724
## 45.00000 36.77673 40.67035
## 45.14286 39.56959 43.46335
## 45.28571 38.39865 42.32210
## 45.42857 37.54012 41.50469
## 45.57143 35.83463 39.95879
## 45.71429 35.54373 39.82921
## 45.85714 35.32937 39.82554
## 46.00000 36.01486 40.66971
## 46.14286 36.46304 41.27304
## 46.28571 37.15266 42.08048
## 46.42857 37.44816 42.49441
## 46.57143 37.79064 42.94188
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 440.5261 619.8409 657.3861 757.0145 732.7112 742.9180 565.9672 463.6168
##  [9] 628.0078 662.1542 758.9955 742.3944 756.0686 584.0946
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 377.2724 343.7879
## 44.85714 540.2756 498.1563
## 45.00000 569.5112 522.9930
## 45.14286 665.9347 617.7200
## 45.28571 638.1786 588.1360
## 45.42857 644.3555 592.1796
## 45.57143 460.6794 404.9434
## 45.71429 345.6442 283.1932
## 45.85714 499.7080 431.7903
## 46.00000 527.2852 455.8899
## 46.14286 619.7475 546.0341
## 46.28571 599.8564 524.4014
## 46.42857 610.0274 532.7179
## 46.57143 434.0404 354.6066
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 503.7799 537.2644
## 44.85714 699.4062 741.5255
## 45.00000 745.2611 791.7793
## 45.14286 848.0942 896.3089
## 45.28571 827.2438 877.2863
## 45.42857 841.4805 893.6564
## 45.57143 671.2550 726.9910
## 45.71429 581.5895 644.0404
## 45.85714 756.3075 824.2253
## 46.00000 797.0232 868.4185
## 46.14286 898.2434 971.9568
## 46.28571 884.9324 960.3875
## 46.42857 902.1098 979.4194
## 46.57143 734.1488 813.5827
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 151.1023 184.2055 185.7007 195.4085 206.0210 222.7650 178.2008 183.9948
##  [9] 210.0252 206.9521 230.3067 214.9254 227.4936 200.6408
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 132.4567 122.58632
## 44.85714 160.7747 148.37122
## 45.00000 157.9281 143.22618
## 45.14286 163.3169 146.32864
## 45.28571 171.1340 152.66587
## 45.42857 184.3405 163.99983
## 45.57143 137.0238 115.22602
## 45.71429 136.9744 112.08328
## 45.85714 157.9042 130.31295
## 46.00000 150.9213 121.26042
## 46.14286 169.8441 137.83720
## 46.28571 150.8005 116.85482
## 46.42857 159.9144 124.14018
## 46.57143 129.3651  91.63403
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 169.7479 179.6183
## 44.85714 207.6363 220.0398
## 45.00000 213.4732 228.1752
## 45.14286 227.5001 244.4884
## 45.28571 240.9081 259.3762
## 45.42857 261.1894 281.5301
## 45.57143 219.3777 241.1755
## 45.71429 231.0153 255.9064
## 45.85714 262.1462 289.7375
## 46.00000 262.9829 292.6439
## 46.14286 290.7692 322.7761
## 46.28571 279.0503 312.9959
## 46.42857 295.0728 330.8471
## 46.57143 271.9166 309.6476
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 323.5262 453.2301 467.9764 469.4522 502.9375 521.0864 473.4912 403.4367
##  [9] 537.2280 546.7913 550.1357 580.1949 598.9605 548.9617
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 290.0761 272.3687
## 44.85714 413.5631 392.5647
## 45.00000 425.2214 402.5882
## 45.14286 422.5000 397.6449
## 45.28571 453.2699 426.9774
## 45.42857 468.2450 440.2725
## 45.57143 418.2600 389.0223
## 45.71429 338.8603 304.6756
## 45.85714 467.5247 430.6260
## 46.00000 473.0717 434.0470
## 46.14286 472.3368 431.1526
## 46.28571 498.9548 455.9490
## 46.42857 514.3332 469.5342
## 46.57143 461.3469 414.9665
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 356.9764 374.6838
## 44.85714 492.8970 513.8955
## 45.00000 510.7314 533.3646
## 45.14286 516.4045 541.2596
## 45.28571 552.6051 578.8976
## 45.42857 573.9278 601.9003
## 45.57143 528.7224 557.9601
## 45.71429 468.0131 502.1977
## 45.85714 606.9313 643.8300
## 46.00000 620.5109 659.5357
## 46.14286 627.9345 669.1187
## 46.28571 661.4349 704.4407
## 46.42857 683.5878 728.3868
## 46.57143 636.5764 682.9569
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 21.95356 20.59901 20.59901 20.59901 20.59901 20.59901 20.59901 20.59901
##  [9] 20.59901 20.59901 20.59901 20.59901 20.59901 20.59901
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429 12.814519 7.976605
## 44.85714 10.993403 5.908499
## 45.00000 10.782642 5.586169
## 45.14286 10.576313 5.270615
## 45.28571 10.374146 4.961427
## 45.42857 10.175899 4.658236
## 45.57143  9.981354 4.360704
## 45.71429  9.790309 4.068527
## 45.85714  9.602584 3.781425
## 46.00000  9.418009 3.499143
## 46.14286  9.236433 3.221445
## 46.28571  9.057712 2.948116
## 46.42857  8.881718 2.678956
## 46.57143  8.708328 2.413779
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 31.09259 35.93051
## 44.85714 30.20463 35.28953
## 45.00000 30.41539 35.61186
## 45.14286 30.62172 35.92741
## 45.28571 30.82388 36.23660
## 45.42857 31.02213 36.53979
## 45.57143 31.21667 36.83732
## 45.71429 31.40772 37.12950
## 45.85714 31.59545 37.41660
## 46.00000 31.78002 37.69889
## 46.14286 31.96160 37.97658
## 46.28571 32.14032 38.24991
## 46.42857 32.31631 38.51907
## 46.57143 32.48970 38.78425
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 4.221489 3.366900 3.366900 3.366900 3.366900 3.366900 3.366900 3.366900
##  [9] 3.366900 3.366900 3.366900 3.366900 3.366900 3.366900
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429  0.7312239 -1.116411
## 44.85714 -0.3749945 -2.355834
## 45.00000 -0.4487923 -2.468698
## 45.14286 -0.5211896 -2.579420
## 45.28571 -0.5922632 -2.688117
## 45.42857 -0.6620833 -2.794898
## 45.57143 -0.7307138 -2.899859
## 45.71429 -0.7982137 -3.003091
## 45.85714 -0.8646369 -3.104677
## 46.00000 -0.9300335 -3.204692
## 46.14286 -0.9944496 -3.303208
## 46.28571 -1.0579280 -3.400290
## 46.42857 -1.1205086 -3.495999
## 46.57143 -1.1822283 -3.590391
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 7.711755  9.559389
## 44.85714 7.108794  9.089633
## 45.00000 7.182591  9.202497
## 45.14286 7.254989  9.313219
## 45.28571 7.326062  9.421916
## 45.42857 7.395882  9.528697
## 45.57143 7.464513  9.633658
## 45.71429 7.532013  9.736890
## 45.85714 7.598436  9.838476
## 46.00000 7.663833  9.938491
## 46.14286 7.728249 10.037007
## 46.28571 7.791727 10.134089
## 46.42857 7.854308 10.229798
## 46.57143 7.916027 10.324190
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 56.71973 65.61800 75.58035 68.70737 83.40104 68.19087 77.14198 68.13148
##  [9] 74.87407 80.24366 75.23755 85.24057 75.87981 81.61677
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%         95%
## 44.71429 34.76570  23.1439358
## 44.85714 39.02333  24.9449662
## 45.00000 45.49923  29.5752533
## 45.14286 37.30133  20.6759666
## 45.28571 49.93672  32.2217826
## 45.42857 30.83203  11.0554527
## 45.57143 37.19273  16.0448773
## 45.71429 22.67309  -1.3911269
## 45.85714 25.55202  -0.5574919
## 46.00000 26.60319  -1.7923588
## 46.14286 18.43906 -11.6282473
## 46.28571 25.64781  -5.8986864
## 46.42857 12.97892 -20.3187988
## 46.57143 15.54090 -19.4375488
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429  78.67377  90.29553
## 44.85714  92.21267 106.29103
## 45.00000 105.66146 121.58544
## 45.14286 100.11342 116.73878
## 45.28571 116.86536 134.58030
## 45.42857 105.54972 125.32629
## 45.57143 117.09122 138.23908
## 45.71429 113.58988 137.65410
## 45.85714 124.19612 150.30564
## 46.00000 133.88413 162.27968
## 46.14286 132.03604 162.10335
## 46.28571 144.83332 176.37982
## 46.42857 138.78071 172.07843
## 46.57143 147.69264 182.67110

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 231.3935 221.8062 234.4630 251.4183 253.9082 259.5845 255.1005 266.3694
##  [9] 266.0630 275.0557 283.3153 287.6594 287.4471 289.8876
## 
## $北海道$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 220.8298 215.2377
## 44.85714 209.6793 203.2598
## 45.00000 219.0924 210.9557
## 45.14286 234.3095 225.2526
## 45.28571 234.4777 224.1918
## 45.42857 238.3843 227.1616
## 45.57143 231.9205 219.6498
## 45.71429 240.1249 226.2319
## 45.85714 237.4187 222.2553
## 46.00000 243.7800 227.2236
## 46.14286 249.6864 231.8843
## 46.28571 251.5996 232.5106
## 46.42857 249.0813 228.7717
## 46.57143 249.1934 227.6511
## 
## $北海道$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 241.9573 247.5494
## 44.85714 233.9330 240.3525
## 45.00000 249.8336 257.9703
## 45.14286 268.5272 277.5841
## 45.28571 273.3388 283.6247
## 45.42857 280.7848 292.0075
## 45.57143 278.2804 290.5512
## 45.71429 292.6139 306.5069
## 45.85714 294.7072 309.8706
## 46.00000 306.3314 322.8878
## 46.14286 316.9443 334.7464
## 46.28571 323.7193 342.8082
## 46.42857 325.8128 346.1224
## 46.57143 330.5819 352.1241
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] -0.3314494  1.3022049  0.3666901  0.9024142  0.5956309  0.7713109
##  [7]  0.6707075  0.7283182  0.6953273  0.7142196  0.7034009  0.7095962
## [13]  0.7060485  0.7080801
## 
## $青森県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -2.885001 -4.236770
## 44.85714 -1.698355 -3.286755
## 45.00000 -2.713014 -4.343311
## 45.14286 -2.402371 -4.151819
## 45.28571 -2.832031 -4.646526
## 45.42857 -2.821020 -4.722685
## 45.57143 -3.052982 -5.024184
## 45.71429 -3.136436 -5.182313
## 45.85714 -3.297590 -5.411313
## 46.00000 -3.407251 -5.589026
## 46.14286 -3.540292 -5.786767
## 46.28571 -3.654252 -5.964334
## 46.42857 -3.773977 -6.145560
## 46.57143 -3.885608 -6.317360
## 
## $青森県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.222102 3.573871
## 44.85714 4.302764 5.891165
## 45.00000 3.446394 5.076691
## 45.14286 4.207200 5.956647
## 45.28571 4.023293 5.837788
## 45.42857 4.363642 6.265307
## 45.57143 4.394397 6.365599
## 45.71429 4.593072 6.638949
## 45.85714 4.688245 6.801968
## 46.00000 4.835690 7.017465
## 46.14286 4.947093 7.193568
## 46.28571 5.073445 7.383527
## 46.42857 5.186074 7.557656
## 46.57143 5.301768 7.733520
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] -1.5899168 -0.6116964  4.3501154  5.2023589  5.9843401  2.9589943
##  [7]  0.9482338  0.1479225  0.5050261  1.4045293  1.4198912  1.3292422
## [13]  1.3292422  1.3292422
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%        95%
## 44.71429 -2.6358958 -3.1896036
## 44.85714 -1.9212479 -2.6144826
## 45.00000  2.8681434  2.0836347
## 45.14286  3.7077999  2.9166281
## 45.28571  4.4880183  3.6959134
## 45.42857  1.4612279  0.6683583
## 45.57143 -0.5509758 -1.3446095
## 45.71429 -1.4281110 -2.2624127
## 45.85714 -1.1167297 -1.9752354
## 46.00000 -0.2534200 -1.1310854
## 46.14286 -0.2442604 -1.1252091
## 46.28571 -0.3353117 -1.2164733
## 46.42857 -0.3378629 -1.2203751
## 46.57143 -0.3404103 -1.2242709
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%         95%
## 44.71429 -0.5439378 0.009769965
## 44.85714  0.6978552 1.391089907
## 45.00000  5.8320875 6.616596077
## 45.14286  6.6969180 7.488089718
## 45.28571  7.4806618 8.272766664
## 45.42857  4.4567607 5.249630332
## 45.57143  2.4474435 3.241077106
## 45.71429  1.7239560 2.558257708
## 45.85714  2.1267819 2.985287556
## 46.00000  3.0624786 3.940144006
## 46.14286  3.0840428 3.964991469
## 46.28571  2.9937960 3.874957647
## 46.42857  2.9963473 3.878859398
## 46.57143  2.9988946 3.882755187
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 14.83809 15.05509 14.88521 15.01820 14.91409 14.99559 14.93179 14.98173
##  [9] 14.94264 14.97324 14.94928 14.96804 14.95336 14.96485
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429  9.856253 7.219027
## 44.85714 10.016069 7.348574
## 45.00000  9.543191 6.715295
## 45.14286  9.587436 6.712565
## 45.28571  9.252335 6.255181
## 45.42857  9.225466 6.170947
## 45.57143  8.971186 5.815833
## 45.71429  8.902337 5.684099
## 45.85714  8.697341 5.391282
## 46.00000  8.604720 5.233427
## 46.14286  8.430805 4.980130
## 46.28571  8.325484 4.809127
## 46.42857  8.171849 4.581935
## 46.57143  8.060434 4.405456
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 19.81993 22.45715
## 44.85714 20.09410 22.76160
## 45.00000 20.22724 23.05513
## 45.14286 20.44896 23.32383
## 45.28571 20.57585 23.57301
## 45.42857 20.76571 23.82023
## 45.57143 20.89240 24.04775
## 45.71429 21.06113 24.27937
## 45.85714 21.18793 24.49399
## 46.00000 21.34177 24.71306
## 46.14286 21.46776 24.91844
## 46.28571 21.61060 25.12695
## 46.42857 21.73487 25.32478
## 46.57143 21.86927 25.52425
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.6588595 0.5214591 0.5214591 0.5214591 0.5214591 0.5214591 0.5214591
##  [8] 0.5214591 0.5214591 0.5214591 0.5214591 0.5214591 0.5214591 0.5214591
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.5769640 -1.231169
## 44.85714 -0.7272531 -1.388281
## 45.00000 -0.7289782 -1.390920
## 45.14286 -0.7307010 -1.393555
## 45.28571 -0.7324214 -1.396186
## 45.42857 -0.7341394 -1.398813
## 45.57143 -0.7358551 -1.401437
## 45.71429 -0.7375685 -1.404057
## 45.85714 -0.7392795 -1.406674
## 46.00000 -0.7409883 -1.409288
## 46.14286 -0.7426947 -1.411897
## 46.28571 -0.7443988 -1.414503
## 46.42857 -0.7461006 -1.417106
## 46.57143 -0.7478001 -1.419705
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 1.894683 2.548888
## 44.85714 1.770171 2.431200
## 45.00000 1.771896 2.433838
## 45.14286 1.773619 2.436473
## 45.28571 1.775340 2.439104
## 45.42857 1.777058 2.441731
## 45.57143 1.778773 2.444355
## 45.71429 1.780487 2.446976
## 45.85714 1.782198 2.449592
## 46.00000 1.783906 2.452206
## 46.14286 1.785613 2.454815
## 46.28571 1.787317 2.457422
## 46.42857 1.789019 2.460024
## 46.57143 1.790718 2.462624
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 1.3638872 1.3864664 0.9581235 1.1268555 1.4911706 1.0200296 1.1130967
##  [8] 0.9586511 0.8772915 0.7984197 0.7230773 0.6520870 0.5860707 0.5254683
## 
## $山形県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%         95%
## 44.71429  0.41767590 -0.08321808
## 44.85714  0.42109860 -0.08993623
## 45.00000 -0.03560695 -0.56165609
## 45.14286  0.09729793 -0.44771691
## 45.28571  0.42075374 -0.14589073
## 45.42857 -0.09376902 -0.68337839
## 45.57143 -0.04438427 -0.65711767
## 45.71429 -0.20517477 -0.82126700
## 45.85714 -0.31764984 -0.95021360
## 46.00000 -0.42275823 -1.06921080
## 46.14286 -0.51975389 -1.17766899
## 46.28571 -0.60822143 -1.27538840
## 46.42857 -0.68801304 -1.36247221
## 46.57143 -0.75919353 -1.43925241
## 
## $山形県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.310098 2.810992
## 44.85714 2.351834 2.862869
## 45.00000 1.951854 2.477903
## 45.14286 2.156413 2.701428
## 45.28571 2.561587 3.128232
## 45.42857 2.133828 2.723438
## 45.57143 2.270578 2.883311
## 45.71429 2.122477 2.738569
## 45.85714 2.072233 2.704797
## 46.00000 2.019598 2.666050
## 46.14286 1.965909 2.623824
## 46.28571 1.912395 2.579562
## 46.42857 1.860154 2.534614
## 46.57143 1.810130 2.490189
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 3.261789 3.261789 3.261789 3.261789 3.261789 3.261789 3.261789 3.261789
##  [9] 3.261789 3.261789 3.261789 3.261789 3.261789 3.261789
## 
## $福島県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%        95%
## 44.71429  0.71810232 -0.6284443
## 44.85714  0.65460397 -0.7255566
## 45.00000  0.59261578 -0.8203594
## 45.14286  0.53203488 -0.9130099
## 45.28571  0.47276956 -1.0036484
## 45.42857  0.41473767 -1.0924005
## 45.57143  0.35786525 -1.1793793
## 45.71429  0.30208546 -1.2646872
## 45.85714  0.24733765 -1.3484167
## 46.00000  0.19356658 -1.4306525
## 46.14286  0.14072176 -1.5114716
## 46.28571  0.08875692 -1.5909450
## 46.42857  0.03762951 -1.6691376
## 46.57143 -0.01269971 -1.7461095
## 
## $福島県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 5.805476 7.152023
## 44.85714 5.868975 7.249135
## 45.00000 5.930963 7.343938
## 45.14286 5.991544 7.436589
## 45.28571 6.050809 7.527227
## 45.42857 6.108841 7.615979
## 45.57143 6.165714 7.702958
## 45.71429 6.221493 7.788266
## 45.85714 6.276241 7.871995
## 46.00000 6.330012 7.954231
## 46.14286 6.382857 8.035050
## 46.28571 6.434822 8.114524
## 46.42857 6.485949 8.192716
## 46.57143 6.536279 8.269688
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 25.47948 25.68456 27.19247 28.38820 28.17550 30.30658 27.98485 28.18533
##  [9] 28.38735 29.32197 29.79266 29.82523 31.07206 29.34631
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 21.51667 19.41888
## 44.85714 21.38809 19.11368
## 45.00000 22.58645 20.14817
## 45.14286 23.49217 20.90036
## 45.28571 23.00568 20.26895
## 45.42857 24.87678 22.00242
## 45.57143 22.30695 19.30126
## 45.71429 22.10186 18.88147
## 45.85714 22.02015 18.64955
## 46.00000 22.68314 19.16876
## 46.14286 22.89289 19.24037
## 46.28571 22.67404 18.88843
## 46.42857 23.67798 19.76380
## 46.57143 21.71709 17.67843
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 29.44230 31.54008
## 44.85714 29.98103 32.25545
## 45.00000 31.79850 34.23678
## 45.14286 33.28424 35.87605
## 45.28571 33.34531 36.08204
## 45.42857 35.73638 38.61074
## 45.57143 33.66274 36.66844
## 45.71429 34.26880 37.48919
## 45.85714 34.75456 38.12516
## 46.00000 35.96080 39.47518
## 46.14286 36.69243 40.34494
## 46.28571 36.97643 40.76204
## 46.42857 38.46613 42.38031
## 46.57143 36.97554 41.01420
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 2.308257 2.850678 2.509742 2.510626 2.479221 2.851527 2.719249 2.561864
##  [9] 2.561864 2.561864 2.561864 2.561864 2.561864 2.561864
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.6649085 -2.238807
## 44.85714 -0.1660503 -1.763010
## 45.00000 -0.5499288 -2.169621
## 45.14286 -0.5913928 -2.233502
## 45.28571 -0.6645762 -2.328802
## 45.42857 -0.3334996 -2.019551
## 45.57143 -0.5064804 -2.214078
## 45.71429 -0.8362769 -2.635144
## 45.85714 -0.8900050 -2.717314
## 46.00000 -0.9429095 -2.798224
## 46.14286 -0.9950272 -2.877931
## 46.28571 -1.0463921 -2.956487
## 46.42857 -1.0970361 -3.033941
## 46.57143 -1.1469886 -3.110336
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 5.281423 6.855322
## 44.85714 5.867406 7.464366
## 45.00000 5.569413 7.189105
## 45.14286 5.612645 7.254755
## 45.28571 5.623017 7.287243
## 45.42857 6.036553 7.722605
## 45.57143 5.944978 7.652576
## 45.71429 5.960004 7.758871
## 45.85714 6.013732 7.841041
## 46.00000 6.066637 7.921951
## 46.14286 6.118754 8.001659
## 46.28571 6.170119 8.080215
## 46.42857 6.220763 8.157668
## 46.57143 6.270716 8.234064
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 5.650053 6.713757 3.626496 6.326087 4.888200 5.773434 5.330746 5.627983
##  [9] 5.497652 5.601876 5.567541 5.606829 5.600640 5.617081
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%       95%
## 44.71429  1.17683743 -1.191140
## 44.85714  1.57708887 -1.142099
## 45.00000 -1.70185538 -4.522514
## 45.14286  0.94309441 -1.906490
## 45.28571 -0.86552544 -3.911364
## 45.42857 -0.06616474 -3.157462
## 45.57143 -0.65760918 -3.827653
## 45.71429 -0.43272931 -3.641076
## 45.85714 -0.64321553 -3.893995
## 46.00000 -0.59389615 -3.873740
## 46.14286 -0.68071621 -3.988344
## 46.28571 -0.68379051 -4.013844
## 46.42857 -0.72912801 -4.079905
## 46.57143 -0.74704677 -4.116012
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429 10.123269 12.49125
## 44.85714 11.850425 14.56961
## 45.00000  8.954847 11.77551
## 45.14286 11.709079 14.55866
## 45.28571 10.641926 13.68776
## 45.42857 11.613033 14.70433
## 45.57143 11.319102 14.48915
## 45.71429 11.688695 14.89704
## 45.85714 11.638520 14.88930
## 46.00000 11.797648 15.07749
## 46.14286 11.815798 15.12343
## 46.28571 11.897449 15.22750
## 46.42857 11.930408 15.28118
## 46.57143 11.981208 15.35017
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  80.06884  86.93842 103.42923  89.17782  98.94507 103.39434  92.40863
##  [8]  94.00214  97.79312 108.07951  99.35856  94.19101 103.09206  99.10890
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 66.14034 58.76704
## 44.85714 72.37736 64.66921
## 45.00000 88.26198 80.23292
## 45.14286 73.42769 65.09007
## 45.28571 82.63287 73.99771
## 45.42857 86.53879 77.61601
## 45.57143 75.02672 65.82530
## 45.71429 74.45922 64.11382
## 45.85714 77.44322 66.67064
## 46.00000 86.95343 75.76997
## 46.14286 77.48384 65.90406
## 46.28571 71.59242 59.62945
## 46.42857 79.79208 67.45782
## 46.57143 75.12804 62.43334
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429  93.99734 101.3706
## 44.85714 101.49947 109.2076
## 45.00000 118.59648 126.6255
## 45.14286 104.92796 113.2656
## 45.28571 115.25728 123.8924
## 45.42857 120.24988 129.1727
## 45.57143 109.79053 118.9919
## 45.71429 113.54505 123.8905
## 45.85714 118.14302 128.9156
## 46.00000 129.20558 140.3890
## 46.14286 121.23329 132.8131
## 46.28571 116.78960 128.7526
## 46.42857 126.39204 138.7263
## 46.57143 123.08976 135.7845
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 56.90445 63.07855 66.98957 70.79669 66.77917 74.63490 67.17683 65.82248
##  [9] 67.59829 70.00464 70.18482 69.11064 71.81732 69.03155
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 45.23661 39.06002
## 44.85714 50.07159 43.18612
## 45.00000 53.51781 46.38629
## 45.14286 56.87564 49.50629
## 45.28571 52.42289 44.82313
## 45.42857 59.85619 52.03282
## 45.57143 51.98745 43.94667
## 45.71429 49.33134 40.60146
## 45.85714 50.38210 41.26840
## 46.00000 52.23898 42.83441
## 46.14286 51.88618 42.19947
## 46.28571 50.29412 40.33325
## 46.42857 52.49678 42.26911
## 46.57143 49.21982 38.73212
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 68.57230  74.74889
## 44.85714 76.08551  82.97098
## 45.00000 80.46133  87.59285
## 45.14286 84.71774  92.08710
## 45.28571 81.13546  88.73521
## 45.42857 89.41360  97.23697
## 45.57143 82.36621  90.40699
## 45.71429 82.31363  91.04351
## 45.85714 84.81448  93.92818
## 46.00000 87.77030  97.17487
## 46.14286 88.48346  98.17017
## 46.28571 87.92717  97.88803
## 46.42857 91.13785 101.36553
## 46.57143 88.84328  99.33097
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 219.6030 321.3197 307.1868 390.8065 362.8223 374.7146 274.0413 234.3562
##  [9] 335.3917 320.8012 403.9783 375.5659 387.0438 285.9696
## 
## $東京都$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 174.1330 150.0626
## 44.85714 268.7711 240.9536
## 45.00000 251.4485 221.9425
## 45.14286 332.2398 301.2364
## 45.28571 301.7267 269.3846
## 45.42857 311.3431 277.7963
## 45.57143 208.6112 173.9747
## 45.71429 161.8569 123.4781
## 45.85714 258.9586 218.4974
## 46.00000 241.5172 199.5468
## 46.14286 322.1156 278.7801
## 46.28571 291.3610 246.7856
## 46.42857 300.7041 254.9987
## 46.57143 197.6784 150.9399
## 
## $東京都$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 265.0729 289.1433
## 44.85714 373.8683 401.6859
## 45.00000 362.9251 392.4311
## 45.14286 449.3732 480.3766
## 45.28571 423.9180 456.2601
## 45.42857 438.0861 471.6330
## 45.57143 339.4713 374.1079
## 45.71429 306.8555 345.2343
## 45.85714 411.8247 452.2859
## 46.00000 400.0852 442.0556
## 46.14286 485.8409 529.1764
## 46.28571 459.7707 504.3461
## 46.42857 473.3834 519.0889
## 46.57143 374.2607 420.9992
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  92.58953 115.60649 130.27301 136.00883 137.85885 137.14982 122.64677
##  [8] 115.67822 123.73253 132.51419 129.91007 130.99003 128.39854 127.22919
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429  72.49361  61.85548
## 44.85714  93.10001  81.18580
## 45.00000 106.92596  94.56678
## 45.14286 112.20098  99.59787
## 45.28571 113.70867 100.92434
## 45.42857 112.70069  99.75810
## 45.57143  97.91670  84.82540
## 45.71429  87.96697  73.29751
## 45.85714  94.85761  79.57215
## 46.00000 102.94171  87.28698
## 46.14286  99.79257  83.84933
## 46.28571 100.38604  84.18526
## 46.42857  97.33399  80.88941
## 46.57143  95.71785  79.03675
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 112.6854 123.3236
## 44.85714 138.1130 150.0272
## 45.00000 153.6201 165.9792
## 45.14286 159.8167 172.4198
## 45.28571 162.0090 174.7934
## 45.42857 161.5989 174.5415
## 45.57143 147.3768 160.4681
## 45.71429 143.3895 158.0589
## 45.85714 152.6075 167.8929
## 46.00000 162.0867 177.7414
## 46.14286 160.0276 175.9708
## 46.28571 161.5940 177.7948
## 46.42857 159.4631 175.9077
## 46.57143 158.7405 175.4216
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 3.100791 3.208681 3.208681 3.208681 3.208681 3.208681 3.208681 3.208681
##  [9] 3.208681 3.208681 3.208681 3.208681 3.208681 3.208681
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%           95%
## 44.71429 1.2777327  0.3126641726
## 44.85714 1.3071209  0.3004955434
## 45.00000 1.2778511  0.2557313088
## 45.14286 1.2490185  0.2116356037
## 45.28571 1.2206039  0.1681793412
## 45.42857 1.1925898  0.1253354847
## 45.57143 1.1649597  0.0830788505
## 45.71429 1.1376982  0.0413859352
## 45.85714 1.1107909  0.0002347626
## 46.00000 1.0842243 -0.0403952517
## 46.14286 1.0579859 -0.0805234207
## 46.28571 1.0320638 -0.1201678931
## 46.42857 1.0064468 -0.1593457495
## 46.57143 0.9811243 -0.1980730882
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 4.923849 5.888917
## 44.85714 5.110242 6.116867
## 45.00000 5.139512 6.161631
## 45.14286 5.168344 6.205727
## 45.28571 5.196759 6.249183
## 45.42857 5.224773 6.292027
## 45.57143 5.252403 6.334284
## 45.71429 5.279664 6.375977
## 45.85714 5.306572 6.417128
## 46.00000 5.333138 6.457758
## 46.14286 5.359377 6.497886
## 46.28571 5.385299 6.537531
## 46.42857 5.410916 6.576708
## 46.57143 5.436238 6.615436
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 1.0087109 0.9874512 1.2110689 1.1098409 1.1316350 1.1352153 1.1686602
##  [8] 1.1658421 1.1774834 1.1850751 1.1959458 1.2023243 1.2100497 1.2167704
## 
## $富山県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.445671 -2.744942
## 44.85714 -1.599294 -2.968634
## 45.00000 -1.524858 -2.973170
## 45.14286 -1.735822 -3.242225
## 45.28571 -1.884242 -3.480750
## 45.42857 -1.977764 -3.625676
## 45.57143 -2.033546 -3.728692
## 45.71429 -2.110377 -3.844703
## 45.85714 -2.168344 -3.939517
## 46.00000 -2.217424 -4.018599
## 46.14286 -2.256422 -4.083995
## 46.28571 -2.293055 -4.143398
## 46.42857 -2.323252 -4.193669
## 46.57143 -2.349305 -4.237071
## 
## $富山県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 3.463093 4.762364
## 44.85714 3.574196 4.943536
## 45.00000 3.946996 5.395308
## 45.14286 3.955504 5.461907
## 45.28571 4.147512 5.744020
## 45.42857 4.248195 5.896107
## 45.57143 4.370867 6.066013
## 45.71429 4.442062 6.176387
## 45.85714 4.523310 6.294484
## 46.00000 4.587575 6.388749
## 46.14286 4.648313 6.475886
## 46.28571 4.697704 6.548046
## 46.42857 4.743352 6.613769
## 46.57143 4.782846 6.670612
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 1.355391 1.355391 1.355391 1.355391 1.355391 1.355391 1.355391 1.355391
##  [9] 1.355391 1.355391 1.355391 1.355391 1.355391 1.355391
## 
## $石川県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -2.321364 -4.267720
## 44.85714 -2.527800 -4.583437
## 45.00000 -2.723802 -4.883196
## 45.14286 -2.910809 -5.169198
## 45.28571 -3.089955 -5.443180
## 45.42857 -3.262157 -5.706539
## 45.57143 -3.428164 -5.960424
## 45.71429 -3.588599 -6.205790
## 45.85714 -3.743990 -6.443439
## 46.00000 -3.894783 -6.674058
## 46.14286 -4.041365 -6.898235
## 46.28571 -4.184069 -7.116482
## 46.42857 -4.323188 -7.329247
## 46.57143 -4.458980 -7.536922
## 
## $石川県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 5.032146  6.978502
## 44.85714 5.238582  7.294219
## 45.00000 5.434584  7.593979
## 45.14286 5.621591  7.879981
## 45.28571 5.800738  8.153962
## 45.42857 5.972939  8.417322
## 45.57143 6.138946  8.671207
## 45.71429 6.299382  8.916572
## 45.85714 6.454772  9.154221
## 46.00000 6.605566  9.384840
## 46.14286 6.752147  9.609017
## 46.28571 6.894852  9.827265
## 46.42857 7.033971 10.040029
## 46.57143 7.169762 10.247705
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 1.0166119 0.3417574 0.4919511 0.8527855 0.4909853 0.6993674 0.7807652
##  [8] 0.6304159 0.7781510 0.7688692 0.7261617 0.8075114 0.7834117 0.7833632
## 
## $福井県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.8127671 -1.781182
## 44.85714 -1.5966269 -2.622745
## 45.00000 -1.6461420 -2.777980
## 45.14286 -1.5284314 -2.788971
## 45.28571 -1.9344436 -3.218388
## 45.42857 -1.8246673 -3.160810
## 45.57143 -1.8148275 -3.188851
## 45.71429 -1.9899443 -3.377079
## 45.85714 -1.8874458 -3.298528
## 46.00000 -1.9214613 -3.345636
## 46.14286 -1.9784409 -3.410171
## 46.28571 -1.9165068 -3.358515
## 46.42857 -1.9503877 -3.397574
## 46.57143 -1.9581990 -3.409494
## 
## $福井県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.845991 3.814406
## 44.85714 2.280142 3.306260
## 45.00000 2.630044 3.761882
## 45.14286 3.234002 4.494542
## 45.28571 2.916414 4.200359
## 45.42857 3.223402 4.559545
## 45.57143 3.376358 4.750382
## 45.71429 3.250776 4.637911
## 45.85714 3.443748 4.854829
## 46.00000 3.459200 4.883375
## 46.14286 3.430764 4.862494
## 46.28571 3.531530 4.973538
## 46.42857 3.517211 4.964397
## 46.57143 3.524925 4.976221
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 3.536191 4.059959 4.238539 4.299426 4.320185 4.327263 4.329677 4.330500
##  [9] 4.330780 4.330876 4.330908 4.330919 4.330923 4.330925
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 1.751553 0.8068223
## 44.85714 2.073587 1.0220653
## 45.00000 2.174437 1.0817681
## 45.14286 2.185762 1.0668563
## 45.28571 2.165417 1.0247509
## 45.42857 2.134478 0.9736883
## 45.57143 2.100286 0.9201180
## 45.71429 2.065349 0.8662505
## 45.85714 2.030510 0.8128210
## 46.00000 1.996043 0.7600580
## 46.14286 1.962028 0.7080178
## 46.28571 1.928476 0.6566990
## 46.42857 1.895380 0.6060818
## 46.57143 1.862726 0.5561414
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 5.320830 6.265561
## 44.85714 6.046331 7.097853
## 45.00000 6.302640 7.395309
## 45.14286 6.413089 7.531995
## 45.28571 6.474954 7.615620
## 45.42857 6.520048 7.680839
## 45.57143 6.559067 7.739235
## 45.71429 6.595650 7.794749
## 45.85714 6.631050 7.848739
## 46.00000 6.665708 7.901693
## 46.14286 6.699789 7.953799
## 46.28571 6.733363 8.005140
## 46.42857 6.766466 8.055765
## 46.57143 6.799123 8.105708
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 14.79358 14.79358 14.79358 14.79358 14.79358 14.79358 14.79358 14.79358
##  [9] 14.79358 14.79358 14.79358 14.79358 14.79358 14.79358
## 
## $長野県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 11.782425 10.188413
## 44.85714 11.584022  9.884981
## 45.00000 11.397188  9.599245
## 45.14286 11.220110  9.328427
## 45.28571 11.051402  9.070410
## 45.42857 10.889978  8.823534
## 45.57143 10.734970  8.586469
## 45.71429 10.585668  8.358131
## 45.85714 10.441484  8.137622
## 46.00000 10.301927  7.924187
## 46.14286 10.166577  7.717187
## 46.28571 10.035075  7.516072
## 46.42857  9.907111  7.320368
## 46.57143  9.782414  7.129660
## 
## $長野県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 17.80474 19.39876
## 44.85714 18.00315 19.70219
## 45.00000 18.18998 19.98792
## 45.14286 18.36706 20.25874
## 45.28571 18.53577 20.51676
## 45.42857 18.69719 20.76364
## 45.57143 18.85220 21.00070
## 45.71429 19.00150 21.22904
## 45.85714 19.14569 21.44955
## 46.00000 19.28524 21.66298
## 46.14286 19.42059 21.86998
## 46.28571 19.55209 22.07110
## 46.42857 19.68006 22.26680
## 46.57143 19.80476 22.45751
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  9.695799 11.296619 10.411090 13.207350 11.103904  8.663158 10.247818
##  [8] 10.274273 10.977616 10.744078 11.608851 10.449336 10.034227 10.503983
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 6.292493 4.4908924
## 44.85714 7.405034 5.3449525
## 45.00000 6.260843 4.0638348
## 45.14286 8.854782 6.5506711
## 45.28571 6.568928 4.1682573
## 45.42857 3.955866 1.4639752
## 45.57143 5.375152 2.7957181
## 45.71429 4.945849 2.1251516
## 45.85714 5.367398 2.3975279
## 46.00000 4.899598 1.8057181
## 46.14286 5.548370 2.3401454
## 46.28571 4.182875 0.8656114
## 46.42857 3.569078 0.1466347
## 46.57143 3.846281 0.3219066
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 13.09910 14.90071
## 44.85714 15.18820 17.24829
## 45.00000 14.56134 16.75834
## 45.14286 17.55992 19.86403
## 45.28571 15.63888 18.03955
## 45.42857 13.37045 15.86234
## 45.57143 15.12048 17.69992
## 45.71429 15.60270 18.42339
## 45.85714 16.58783 19.55770
## 46.00000 16.58856 19.68244
## 46.14286 17.66933 20.87756
## 46.28571 16.71580 20.03306
## 46.42857 16.49938 19.92182
## 46.57143 17.16169 20.68606
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 16.41605 22.89490 26.66064 20.39249 20.43755 29.10050 24.56188 18.98272
##  [9] 24.26562 27.00712 21.12418 21.93411 28.21957 24.61229
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 12.39653 10.268723
## 44.85714 18.26228 15.809922
## 45.00000 21.81612 19.251582
## 45.14286 15.35338 12.685829
## 45.28571 14.94124 12.031670
## 45.42857 23.30895 20.243095
## 45.57143 18.62980 15.489553
## 45.71429 12.67903  9.342058
## 45.85714 17.58340 14.046051
## 46.00000 20.15125 16.521964
## 46.14286 14.06851 10.333463
## 46.28571 14.53938 10.624841
## 46.42857 20.60280 16.570722
## 46.57143 16.84396 12.731659
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 20.43557 22.56338
## 44.85714 27.52752 29.97988
## 45.00000 31.50517 34.06970
## 45.14286 25.43161 28.09916
## 45.28571 25.93386 28.84343
## 45.42857 34.89204 37.95790
## 45.57143 30.49396 33.63421
## 45.71429 25.28641 28.62338
## 45.85714 30.94783 34.48518
## 46.00000 33.86300 37.49228
## 46.14286 28.17986 31.91491
## 46.28571 29.32885 33.24339
## 46.42857 35.83635 39.86843
## 46.57143 32.38061 36.49292
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  96.07132 123.60787 117.38897 134.55794 134.37448 146.85135 113.50872
##  [8] 112.27417 133.79087 123.70628 141.92778 138.19179 146.54110 126.94881
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429  81.34184 73.54452
## 44.85714 104.86019 94.93577
## 45.00000  94.27702 82.04230
## 45.14286 107.73764 93.53982
## 45.28571 105.09143 89.58991
## 45.42857 114.26219 97.01053
## 45.57143  78.65745 60.20830
## 45.71429  72.52938 51.48976
## 45.85714  89.80501 66.52031
## 46.00000  76.28949 51.18856
## 46.14286  90.63676 63.48494
## 46.28571  83.84896 55.08160
## 46.42857  89.11391 58.71380
## 46.57143  66.41717 34.37366
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 110.8008 118.5981
## 44.85714 142.3556 152.2800
## 45.00000 140.5009 152.7356
## 45.14286 161.3783 175.5761
## 45.28571 163.6575 179.1591
## 45.42857 179.4405 196.6922
## 45.57143 148.3600 166.8091
## 45.71429 152.0190 173.0586
## 45.85714 177.7767 201.0614
## 46.00000 171.1231 196.2240
## 46.14286 193.2188 220.3706
## 46.28571 192.5346 221.3020
## 46.42857 203.9683 234.3684
## 46.57143 187.4805 219.5240
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 4.206842 4.206842 4.206842 4.206842 4.206842 4.206842 4.206842 4.206842
##  [9] 4.206842 4.206842 4.206842 4.206842 4.206842 4.206842
## 
## $三重県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%       95%
## 44.71429  0.51576046 -1.438180
## 44.85714  0.31162736 -1.750375
## 45.00000  0.11767201 -2.047004
## 45.14286 -0.06749132 -2.330187
## 45.28571 -0.24495982 -2.601601
## 45.42857 -0.41561986 -2.862603
## 45.57143 -0.58019965 -3.114306
## 45.71429 -0.73930619 -3.357639
## 45.85714 -0.89345172 -3.593384
## 46.00000 -1.04307325 -3.822210
## 46.14286 -1.18854717 -4.044694
## 46.28571 -1.33020039 -4.261334
## 46.42857 -1.46831901 -4.472568
## 46.57143 -1.60315513 -4.678782
## 
## $三重県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429  7.897923  9.851864
## 44.85714  8.102056 10.164058
## 45.00000  8.296012 10.460688
## 45.14286  8.481175 10.743870
## 45.28571  8.658644 11.015285
## 45.42857  8.829304 11.276287
## 45.57143  8.993883 11.527990
## 45.71429  9.152990 11.771323
## 45.85714  9.307136 12.007068
## 46.00000  9.456757 12.235894
## 46.14286  9.602231 12.458377
## 46.28571  9.743884 12.675017
## 46.42857  9.882003 12.886252
## 46.57143 10.016839 13.092466
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 7.995620 7.674484 7.664109 7.560730 7.554513 7.521153 7.518226 7.507435
##  [9] 7.506194 7.502695 7.502199 7.501062 7.500871 7.500501
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%         95%
## 44.71429 4.104108  2.04406545
## 44.85714 3.721892  1.62951636
## 45.00000 3.299122  0.98843800
## 45.14286 3.112328  0.75748601
## 45.28571 2.946580  0.50728750
## 45.42857 2.827618  0.34301114
## 45.57143 2.720534  0.18078894
## 45.71429 2.626478  0.04265624
## 45.85714 2.537849 -0.09223372
## 46.00000 2.453922 -0.21873743
## 46.14286 2.372711 -0.34267630
## 46.28571 2.293743 -0.46284513
## 46.42857 2.216432 -0.58098073
## 46.57143 2.140547 -0.69684119
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 11.88713 13.94717
## 44.85714 11.62708 13.71945
## 45.00000 12.02910 14.33978
## 45.14286 12.00913 14.36397
## 45.28571 12.16245 14.60174
## 45.42857 12.21469 14.69930
## 45.57143 12.31592 14.85566
## 45.71429 12.38839 14.97221
## 45.85714 12.47454 15.10462
## 46.00000 12.55147 15.22413
## 46.14286 12.63169 15.34707
## 46.28571 12.70838 15.46497
## 46.42857 12.78531 15.58272
## 46.57143 12.86045 15.69784
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 17.57486 18.91832 18.70601 18.88920 22.23257 20.83311 20.78555 18.82216
##  [9] 19.34464 20.08310 19.94327 21.13465 20.53048 20.27874
## 
## $京都府$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 11.24151  7.888839
## 44.85714 11.98870  8.320374
## 45.00000 11.54547  7.754916
## 45.14286 11.57016  7.695698
## 45.28571 14.77472 10.826773
## 45.42857 13.24341  9.225662
## 45.57143 13.06749  8.981792
## 45.71429 10.70594  6.409469
## 45.85714 11.01067  6.598934
## 46.00000 11.57439  7.070155
## 46.14286 11.27322  6.683578
## 46.28571 12.30894  7.636887
## 46.42857 11.55256  6.799938
## 46.57143 11.15137  6.319637
## 
## $京都府$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 23.90821 27.26088
## 44.85714 25.84795 29.51627
## 45.00000 25.86654 29.65709
## 45.14286 26.20823 30.08269
## 45.28571 29.69042 33.63837
## 45.42857 28.42281 32.44055
## 45.57143 28.50361 32.58930
## 45.71429 26.93838 31.23485
## 45.85714 27.67860 32.09034
## 46.00000 28.59180 33.09604
## 46.14286 28.61332 33.20296
## 46.28571 29.96036 34.63241
## 46.42857 29.50839 34.26101
## 46.57143 29.40611 34.23785
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 215.1162 304.6019 288.8825 294.5938 321.8849 338.6541 313.1398 270.0724
##  [9] 356.9019 356.9820 353.0837 376.5475 392.0301 373.6393
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 190.7446 177.8431
## 44.85714 277.4445 263.0683
## 45.00000 259.1995 243.4864
## 45.14286 262.5840 245.6391
## 45.28571 287.7063 269.6133
## 45.42857 302.4363 283.2637
## 45.57143 274.9917 254.7973
## 45.71429 226.4264 203.3216
## 45.85714 310.2354 285.5317
## 46.00000 307.4790 281.2737
## 46.14286 300.8982 273.2728
## 46.28571 321.8107 292.8348
## 46.42857 334.8557 304.5895
## 46.57143 314.1272 282.6234
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 239.4878 252.3893
## 44.85714 331.7594 346.1356
## 45.00000 318.5654 334.2786
## 45.14286 326.6036 343.5486
## 45.28571 356.0635 374.1566
## 45.42857 374.8719 394.0444
## 45.57143 351.2879 371.4823
## 45.71429 313.7184 336.8232
## 45.85714 403.5684 428.2721
## 46.00000 406.4850 432.6903
## 46.14286 405.2693 432.8947
## 46.28571 431.2843 460.2602
## 46.42857 449.2044 479.4707
## 46.57143 433.1515 464.6553
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  67.26291  84.89272  90.46003  95.56340  89.99609  94.63551  94.63551
##  [8]  89.19018  97.36939  99.95230 102.31997  99.73706 101.88949 101.88949
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 57.90996 52.95880
## 44.85714 74.81425 69.47903
## 45.00000 79.70487 74.01144
## 45.14286 84.17168 78.14127
## 45.28571 78.00154 71.65202
## 45.42857 82.06702 75.41367
## 45.57143 81.51817 74.57426
## 45.71429 73.77666 65.61724
## 45.85714 81.00510 72.34237
## 46.00000 82.68953 73.55117
## 46.14286 84.20322 74.61278
## 46.28571 80.80481 70.78268
## 46.42857 82.17544 71.73945
## 46.57143 81.42349 70.58944
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429  76.61587  81.56702
## 44.85714  94.97119 100.30641
## 45.00000 101.21519 106.90862
## 45.14286 106.95511 112.98552
## 45.28571 101.99063 108.34016
## 45.42857 107.20400 113.85735
## 45.57143 107.75285 114.69676
## 45.71429 104.60370 112.76312
## 45.85714 113.73368 122.39641
## 46.00000 117.21508 126.35344
## 46.14286 120.43673 130.02716
## 46.28571 118.66931 128.69144
## 46.42857 121.60353 132.03952
## 46.57143 122.35548 133.18953
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 15.20130 11.24673 16.29978 16.66196 16.34382 16.18661 14.88606 15.19706
##  [9] 15.14919 15.53550 15.67538 15.60041 15.54654 15.42730
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429 11.378985 9.355575
## 44.85714  7.249590 5.133629
## 45.00000 11.787171 9.398341
## 45.14286 12.086016 9.663654
## 45.28571 11.685604 9.219691
## 45.42857 11.381549 8.837904
## 45.57143  9.931929 7.309370
## 45.71429 10.015765 7.272953
## 45.85714  9.798036 6.965307
## 46.00000 10.023451 7.105547
## 46.14286 10.027282 7.037359
## 46.28571  9.821653 6.762562
## 46.42857  9.630658 6.498979
## 46.57143  9.374550 6.170418
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 19.02361 21.04702
## 44.85714 15.24388 17.35984
## 45.00000 20.81239 23.20122
## 45.14286 21.23791 23.66028
## 45.28571 21.00204 23.46796
## 45.42857 20.99167 23.53532
## 45.57143 19.84019 22.46275
## 45.71429 20.37835 23.12116
## 45.85714 20.50034 23.33307
## 46.00000 21.04755 23.96546
## 46.14286 21.32348 24.31340
## 46.28571 21.37917 24.43826
## 46.42857 21.46242 24.59410
## 46.57143 21.48005 24.68418
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 7.030250 6.944984 6.160097 6.303162 6.629481 5.617504 5.996052 6.165421
##  [9] 6.154647 6.163946 6.155920 6.162847 6.156869 6.162029
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 5.091282 4.064855
## 44.85714 4.824922 3.702630
## 45.00000 3.965472 2.803707
## 45.14286 3.961175 2.721403
## 45.28571 4.211487 2.931478
## 45.42857 3.074338 1.728067
## 45.57143 3.376726 1.990138
## 45.71429 3.502849 2.093369
## 45.85714 3.442023 2.006047
## 46.00000 3.370926 1.892391
## 46.14286 3.311412 1.805620
## 46.28571 3.245468 1.701100
## 46.42857 3.187406 1.615467
## 46.57143 3.125414 1.517928
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 8.969217  9.995645
## 44.85714 9.065045 10.187338
## 45.00000 8.354723  9.516488
## 45.14286 8.645148  9.884920
## 45.28571 9.047475 10.327483
## 45.42857 8.160670  9.506941
## 45.57143 8.615378 10.001966
## 45.71429 8.827993 10.237473
## 45.85714 8.867271 10.303248
## 46.00000 8.956966 10.435501
## 46.14286 9.000429 10.506221
## 46.28571 9.080227 10.624594
## 46.42857 9.126332 10.698270
## 46.57143 9.198643 10.806129
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.3543137 0.2480440 0.2773197 0.3222197 0.3154350 0.3244795 0.3316784
##  [8] 0.3326834 0.3350697 0.3366007 0.3372440 0.3378781 0.3382725 0.3384992
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.5852589 -1.082639
## 44.85714 -0.7012268 -1.203740
## 45.00000 -0.6829421 -1.191274
## 45.14286 -0.6569437 -1.175281
## 45.28571 -0.6666798 -1.186580
## 45.42857 -0.6599979 -1.181149
## 45.57143 -0.6546373 -1.176761
## 45.71429 -0.6544672 -1.177033
## 45.85714 -0.6527321 -1.175643
## 46.00000 -0.6517077 -1.174887
## 46.14286 -0.6514477 -1.174829
## 46.28571 -0.6511491 -1.174708
## 46.42857 -0.6510551 -1.174774
## 46.57143 -0.6511041 -1.174968
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 1.293886 1.791266
## 44.85714 1.197315 1.699828
## 45.00000 1.237581 1.745913
## 45.14286 1.301383 1.819721
## 45.28571 1.297550 1.817450
## 45.42857 1.308957 1.830108
## 45.57143 1.317994 1.840118
## 45.71429 1.319834 1.842400
## 45.85714 1.322872 1.845782
## 46.00000 1.324909 1.848088
## 46.14286 1.325936 1.849318
## 46.28571 1.326905 1.850465
## 46.42857 1.327600 1.851318
## 46.57143 1.328102 1.851967
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843
##  [8] 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843 0.4607843
## 
## $島根県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 -6.31023 -9.894589
## 44.85714 -6.31023 -9.894589
## 45.00000 -6.31023 -9.894589
## 45.14286 -6.31023 -9.894589
## 45.28571 -6.31023 -9.894589
## 45.42857 -6.31023 -9.894589
## 45.57143 -6.31023 -9.894589
## 45.71429 -6.31023 -9.894589
## 45.85714 -6.31023 -9.894589
## 46.00000 -6.31023 -9.894589
## 46.14286 -6.31023 -9.894589
## 46.28571 -6.31023 -9.894589
## 46.42857 -6.31023 -9.894589
## 46.57143 -6.31023 -9.894589
## 
## $島根県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 7.231799 10.81616
## 44.85714 7.231799 10.81616
## 45.00000 7.231799 10.81616
## 45.14286 7.231799 10.81616
## 45.28571 7.231799 10.81616
## 45.42857 7.231799 10.81616
## 45.57143 7.231799 10.81616
## 45.71429 7.231799 10.81616
## 45.85714 7.231799 10.81616
## 46.00000 7.231799 10.81616
## 46.14286 7.231799 10.81616
## 46.28571 7.231799 10.81616
## 46.42857 7.231799 10.81616
## 46.57143 7.231799 10.81616
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 13.620737 10.091124 11.018664 11.415578 10.940033 10.734314 11.421222
##  [8] 11.688744 11.720039 10.430874 10.091072  9.509783  9.646007  9.011129
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429 10.953311 9.541261
## 44.85714  7.056629 5.450265
## 45.00000  7.835789 6.150877
## 45.14286  8.141637 6.408517
## 45.28571  7.594240 5.823084
## 45.42857  7.324251 5.519072
## 45.57143  7.950379 6.113026
## 45.71429  8.103584 6.205715
## 45.85714  8.051246 6.109104
## 46.00000  6.689887 4.709529
## 46.14286  6.282843 4.266888
## 46.28571  5.636857 3.586654
## 46.42857  5.709984 3.626379
## 46.57143  5.013214 2.896846
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 16.28816 17.70021
## 44.85714 13.12562 14.73198
## 45.00000 14.20154 15.88645
## 45.14286 14.68952 16.42264
## 45.28571 14.28583 16.05698
## 45.42857 14.14438 15.94955
## 45.57143 14.89206 16.72942
## 45.71429 15.27390 17.17177
## 45.85714 15.38883 17.33097
## 46.00000 14.17186 16.15222
## 46.14286 13.89930 15.91526
## 46.28571 13.38271 15.43291
## 46.42857 13.58203 15.66563
## 46.57143 13.00904 15.12541
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 5.353788 6.023107 6.759213 6.246810 5.962707 6.295917 6.376938 6.182181
##  [9] 6.180156 6.284407 6.263451 6.212305 6.234288 6.256907
## 
## $広島県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%         95%
## 44.71429  1.83205562 -0.03223707
## 44.85714  2.10635263  0.03294799
## 45.00000  2.64646049  0.46930079
## 45.14286  1.52715323 -0.97128239
## 45.28571  0.69654415 -2.09119402
## 45.42857  0.74146486 -2.19888446
## 45.57143  0.51437439 -2.58907949
## 45.71429 -0.06434201 -3.37105144
## 45.85714 -0.39061362 -3.86896878
## 46.00000 -0.55940928 -4.18230702
## 46.14286 -0.86743604 -4.64229955
## 46.28571 -1.20757117 -5.13541622
## 46.42857 -1.44860190 -5.51567788
## 46.57143 -1.67696850 -5.87690824
## 
## $広島県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429  8.875521 10.73981
## 44.85714  9.939862 12.01327
## 45.00000 10.871966 13.04913
## 45.14286 10.966468 13.46490
## 45.28571 11.228870 14.01661
## 45.42857 11.850369 14.79072
## 45.57143 12.239502 15.34296
## 45.71429 12.428704 15.73541
## 45.85714 12.750925 16.22928
## 46.00000 13.128224 16.75112
## 46.14286 13.394338 17.16920
## 46.28571 13.632181 17.56003
## 46.42857 13.917177 17.98425
## 46.57143 14.190782 18.39072
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 7.502023 7.502023 7.502023 7.502023 7.502023 7.502023 7.502023 7.502023
##  [9] 7.502023 7.502023 7.502023 7.502023 7.502023 7.502023
## 
## $山口県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 5.369807 4.241079
## 44.85714 5.256925 4.068441
## 45.00000 5.149453 3.904077
## 45.14286 5.046681 3.746901
## 45.28571 4.948041 3.596044
## 45.42857 4.853071 3.450800
## 45.57143 4.761391 3.310587
## 45.71429 4.672679 3.174915
## 45.85714 4.586666 3.043369
## 46.00000 4.503119 2.915595
## 46.14286 4.421837 2.791285
## 46.28571 4.342646 2.670172
## 46.42857 4.265391 2.552022
## 46.57143 4.189938 2.436626
## 
## $山口県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%      95%
## 44.71429  9.634240 10.76297
## 44.85714  9.747122 10.93561
## 45.00000  9.854594 11.09997
## 45.14286  9.957366 11.25715
## 45.28571 10.056006 11.40800
## 45.42857 10.150976 11.55325
## 45.57143 10.242656 11.69346
## 45.71429 10.331367 11.82913
## 45.85714 10.417380 11.96068
## 46.00000 10.500928 12.08845
## 46.14286 10.582210 12.21276
## 46.28571 10.661401 12.33387
## 46.42857 10.738655 12.45202
## 46.57143 10.814108 12.56742
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  0.4502399  0.6807866  0.3681503  0.3640595 -0.1475278  0.7551288
##  [7]  0.4074443  0.3255716  0.2217420  0.4909568  0.4922160  0.5685694
## [13]  0.2871297  0.4816034
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.278420 -2.193517
## 44.85714 -1.130396 -2.089179
## 45.00000 -1.461770 -2.430472
## 45.14286 -1.484409 -2.462929
## 45.28571 -2.014360 -3.002602
## 45.42857 -1.129888 -2.127756
## 45.57143 -1.495584 -2.502986
## 45.71429 -1.670784 -2.727590
## 45.85714 -1.808275 -2.882900
## 46.00000 -1.563031 -2.650346
## 46.14286 -1.585466 -2.685325
## 46.28571 -1.532540 -2.644801
## 46.42857 -1.837149 -2.961674
## 46.57143 -1.665594 -2.802252
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.178900 3.093997
## 44.85714 2.491969 3.450752
## 45.00000 2.198071 3.166773
## 45.14286 2.212528 3.191049
## 45.28571 1.719305 2.707546
## 45.42857 2.640146 3.638014
## 45.57143 2.310473 3.317875
## 45.71429 2.321927 3.378733
## 45.85714 2.251759 3.326384
## 46.00000 2.544944 3.632260
## 46.14286 2.569898 3.669757
## 46.28571 2.669679 3.781939
## 46.42857 2.411408 3.535934
## 46.57143 2.628801 3.765459
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.8154613 0.7046128 0.7046128 0.7046128 0.7046128 0.7046128 0.7046128
##  [8] 0.7046128 0.7046128 0.7046128 0.7046128 0.7046128 0.7046128 0.7046128
## 
## $香川県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.5496867 -1.272352
## 44.85714 -0.7241289 -1.480459
## 45.00000 -0.7284289 -1.487035
## 45.14286 -0.7327160 -1.493592
## 45.28571 -0.7369903 -1.500129
## 45.42857 -0.7412520 -1.506647
## 45.57143 -0.7455012 -1.513145
## 45.71429 -0.7497380 -1.519625
## 45.85714 -0.7539625 -1.526086
## 46.00000 -0.7581748 -1.532528
## 46.14286 -0.7623750 -1.538951
## 46.28571 -0.7665631 -1.545357
## 46.42857 -0.7707395 -1.551744
## 46.57143 -0.7749040 -1.558113
## 
## $香川県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.180609 2.903275
## 44.85714 2.133355 2.889685
## 45.00000 2.137654 2.896261
## 45.14286 2.141942 2.902817
## 45.28571 2.146216 2.909355
## 45.42857 2.150478 2.915872
## 45.57143 2.154727 2.922371
## 45.71429 2.158964 2.928850
## 45.85714 2.163188 2.935311
## 46.00000 2.167400 2.941753
## 46.14286 2.171601 2.948177
## 46.28571 2.175789 2.954582
## 46.42857 2.179965 2.960969
## 46.57143 2.184130 2.967338
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 1.9938832 1.4887036 0.8890836 1.2938677 0.8021469 1.1354212 0.7321753
##  [8] 1.0065631 0.6758664 0.9017644 0.6305591 0.8165301 0.5941094 0.7472052
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%        95%
## 44.71429  0.3025468 -0.5927927
## 44.85714 -0.2454833 -1.1635064
## 45.00000 -0.8695489 -1.8005128
## 45.14286 -0.4661105 -1.3977866
## 45.28571 -0.9738514 -1.9140081
## 45.42857 -0.6414192 -1.5820216
## 45.57143 -1.0552099 -2.0013945
## 45.71429 -0.7813502 -1.7278143
## 45.85714 -1.1190076 -2.0691565
## 46.00000 -0.8934410 -1.8437653
## 46.14286 -1.1692498 -2.1220110
## 46.28571 -0.9834867 -1.9363581
## 46.42857 -1.2089557 -2.1634407
## 46.57143 -1.0559903 -2.0105443
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 3.685220 4.580559
## 44.85714 3.222891 4.140914
## 45.00000 2.647716 3.578680
## 45.14286 3.053846 3.985522
## 45.28571 2.578145 3.518302
## 45.42857 2.912262 3.852864
## 45.57143 2.519561 3.465745
## 45.71429 2.794476 3.740940
## 45.85714 2.470740 3.420889
## 46.00000 2.696970 3.647294
## 46.14286 2.430368 3.383129
## 46.28571 2.616547 3.569418
## 46.42857 2.397175 3.351660
## 46.57143 2.550401 3.504955
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.1460619 0.1687243 0.1896650 0.2090150 0.2268950 0.2434168 0.2586834
##  [8] 0.2727903 0.2858256 0.2978706 0.3090006 0.3192851 0.3287883 0.3375696
## 
## $高知県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.548729 -2.445897
## 44.85714 -1.547782 -2.456445
## 45.00000 -1.545167 -2.463532
## 45.14286 -1.541313 -2.467881
## 45.28571 -1.536557 -2.470072
## 45.42857 -1.531163 -2.470569
## 45.57143 -1.525343 -2.469750
## 45.71429 -1.519263 -2.467919
## 45.85714 -1.513053 -2.465322
## 46.00000 -1.506815 -2.462158
## 46.14286 -1.500629 -2.458589
## 46.28571 -1.494555 -2.454743
## 46.42857 -1.488639 -2.450726
## 46.57143 -1.482915 -2.446621
## 
## $高知県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 1.840853 2.738021
## 44.85714 1.885230 2.793894
## 45.00000 1.924498 2.842862
## 45.14286 1.959343 2.885911
## 45.28571 1.990347 2.923862
## 45.42857 2.017997 2.957403
## 45.57143 2.042710 2.987117
## 45.71429 2.064844 3.013500
## 45.85714 2.084704 3.036973
## 46.00000 2.102556 3.057899
## 46.14286 2.118630 3.076590
## 46.28571 2.133125 3.093314
## 46.42857 2.146215 3.108303
## 46.57143 2.158054 3.121760
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 13.40967 18.62863 18.94674 16.33112 15.06151 11.67664 12.90061 14.70376
##  [9] 18.58967 18.62041 17.07757 15.08472 13.24723 14.14082
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                  80%        95%
## 44.71429  -0.1150116  -7.274545
## 44.85714   2.7402489  -5.670551
## 45.00000   1.7832477  -7.302555
## 45.14286  -1.5258291 -10.978726
## 45.28571  -3.4009002 -13.174310
## 45.42857  -7.6740756 -17.917728
## 45.57143  -7.7958474 -18.751891
## 45.71429  -8.5900443 -20.921042
## 45.85714  -6.5363719 -19.837295
## 46.00000  -7.7221003 -21.666985
## 46.14286 -10.0564274 -24.420299
## 46.28571 -12.7382786 -27.466885
## 46.42857 -15.4386424 -30.624026
## 46.57143 -15.7205808 -31.528250
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 26.93435 34.09388
## 44.85714 34.51701 42.92780
## 45.00000 36.11023 45.19603
## 45.14286 34.18807 43.64096
## 45.28571 33.52392 43.29733
## 45.42857 31.02737 41.27102
## 45.57143 33.59707 44.55311
## 45.71429 37.99757 50.32857
## 45.85714 43.71571 57.01664
## 46.00000 44.96293 58.90781
## 46.14286 44.21157 58.57544
## 46.28571 42.90772 57.63633
## 46.42857 41.93310 57.11849
## 46.57143 44.00222 59.80989
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991
##  [8] 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991 0.9233991
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.072171 -2.128562
## 44.85714 -1.147292 -2.243450
## 45.00000 -1.219782 -2.354313
## 45.14286 -1.289899 -2.461548
## 45.28571 -1.357862 -2.565488
## 45.42857 -1.423858 -2.666420
## 45.57143 -1.488048 -2.764591
## 45.71429 -1.550573 -2.860215
## 45.85714 -1.611557 -2.953482
## 46.00000 -1.671108 -3.044557
## 46.14286 -1.729322 -3.133588
## 46.28571 -1.786286 -3.220706
## 46.42857 -1.842076 -3.306031
## 46.57143 -1.896764 -3.389668
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.918969 3.975360
## 44.85714 2.994090 4.090248
## 45.00000 3.066580 4.201112
## 45.14286 3.136697 4.308346
## 45.28571 3.204660 4.412286
## 45.42857 3.270656 4.513218
## 45.57143 3.334846 4.611389
## 45.71429 3.397372 4.707014
## 45.85714 3.458355 4.800280
## 46.00000 3.517906 4.891355
## 46.14286 3.576120 4.980386
## 46.28571 3.633084 5.067504
## 46.42857 3.688875 5.152829
## 46.57143 3.743562 5.236466
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.4646206 0.7388986 0.5983582 0.7577559 1.0865729 0.6138669 0.8328145
##  [8] 0.7359466 0.7855858 0.7601630 0.7487946 0.8240516 0.7642827 0.8935744
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.664847 -2.792119
## 44.85714 -1.630286 -2.884457
## 45.00000 -2.259053 -3.771676
## 45.14286 -2.386071 -4.050312
## 45.28571 -2.378790 -4.213242
## 45.42857 -3.117127 -5.092196
## 45.57143 -3.159663 -5.273152
## 45.71429 -3.646119 -5.965844
## 45.85714 -3.882518 -6.353662
## 46.00000 -4.211299 -6.843032
## 46.14286 -4.491931 -7.266204
## 46.28571 -4.680828 -7.594935
## 46.42857 -4.988678 -8.034112
## 46.57143 -5.099150 -8.271507
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 2.594088  3.721360
## 44.85714 3.108083  4.362254
## 45.00000 3.455770  4.968392
## 45.14286 3.901583  5.565824
## 45.28571 4.551936  6.386388
## 45.42857 4.344861  6.319930
## 45.57143 4.825292  6.938781
## 45.71429 5.118012  7.437738
## 45.85714 5.453689  7.924834
## 46.00000 5.731625  8.363358
## 46.14286 5.989520  8.763793
## 46.28571 6.328931  9.243038
## 46.42857 6.517243  9.562677
## 46.57143 6.886299 10.058656
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1]  7.052271 11.007116 10.363634 10.363634 10.363634 10.363634 10.363634
##  [8] 10.363634 10.363634 10.363634 10.363634 10.363634 10.363634 10.363634
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%         95%
## 44.71429 2.782058  0.52154350
## 44.85714 5.351607  2.35776137
## 45.00000 4.543355  1.46228590
## 45.14286 4.213116  0.95722939
## 45.28571 3.899728  0.47794246
## 45.42857 3.600846  0.02084198
## 45.57143 3.314625 -0.41689457
## 45.71429 3.039581 -0.83753762
## 45.85714 2.774499 -1.24294583
## 46.00000 2.518369 -1.63466358
## 46.14286 2.270340 -2.01399074
## 46.28571 2.029690 -2.38203364
## 46.42857 1.795796 -2.73974317
## 46.57143 1.568120 -3.08794370
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 11.32248 13.58300
## 44.85714 16.66262 19.65647
## 45.00000 16.18391 19.26498
## 45.14286 16.51415 19.77004
## 45.28571 16.82754 20.24932
## 45.42857 17.12642 20.70643
## 45.57143 17.41264 21.14416
## 45.71429 17.68769 21.56480
## 45.85714 17.95277 21.97021
## 46.00000 18.20890 22.36193
## 46.14286 18.45693 22.74126
## 46.28571 18.69758 23.10930
## 46.42857 18.93147 23.46701
## 46.57143 19.15915 23.81521
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 0.2921831 0.3703701 0.3159724 0.4156784 0.4206442 0.4253468 0.4298001
##  [8] 0.4340174 0.4380112 0.4417933 0.4453749 0.4487667 0.4519788 0.4550206
## 
## $大分県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -1.080465 -1.807101
## 44.85714 -1.209711 -2.046155
## 45.00000 -1.432571 -2.358194
## 45.14286 -1.396861 -2.356362
## 45.28571 -1.401295 -2.365771
## 45.42857 -1.404980 -2.373896
## 45.57143 -1.408016 -2.380898
## 45.71429 -1.410490 -2.386914
## 45.85714 -1.412476 -2.392065
## 46.00000 -1.414041 -2.396460
## 46.14286 -1.415241 -2.400192
## 46.28571 -1.416127 -2.403342
## 46.42857 -1.416743 -2.405985
## 46.57143 -1.417128 -2.408183
## 
## $大分県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 1.664831 2.391467
## 44.85714 1.950451 2.786895
## 45.00000 2.064516 2.990139
## 45.14286 2.228218 3.187719
## 45.28571 2.242583 3.207059
## 45.42857 2.255674 3.224590
## 45.57143 2.267617 3.240498
## 45.71429 2.278525 3.254948
## 45.85714 2.288499 3.268088
## 46.00000 2.297627 3.280047
## 46.14286 2.305991 3.290941
## 46.28571 2.313661 3.300876
## 46.42857 2.320701 3.309942
## 46.57143 2.327169 3.318224
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] -0.0032483695 -0.0006087070 -0.0023460782 -0.0006814616 -0.0022763703
##  [6] -0.0007482504 -0.0022123783 -0.0008095626 -0.0021536336 -0.0008658473
## [11] -0.0020997060 -0.0009175167 -0.0020502003 -0.0009649493
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                80%       95%
## 44.71429 -2.675500 -4.090104
## 44.85714 -2.792203 -4.269984
## 45.00000 -3.149552 -4.815582
## 45.14286 -3.396066 -5.193474
## 45.28571 -3.692643 -5.646206
## 45.42857 -3.907268 -5.975254
## 45.57143 -4.165291 -6.369091
## 45.71429 -4.358589 -6.665459
## 45.85714 -4.589357 -7.017676
## 46.00000 -4.767110 -7.290208
## 46.14286 -4.977308 -7.611024
## 46.28571 -5.143072 -7.865165
## 46.42857 -5.337049 -8.161226
## 46.57143 -5.493191 -8.400600
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 2.669003 4.083607
## 44.85714 2.790986 4.268767
## 45.00000 3.144859 4.810889
## 45.14286 3.394703 5.192111
## 45.28571 3.688091 5.641653
## 45.42857 3.905771 5.973758
## 45.57143 4.160866 6.364667
## 45.71429 4.356970 6.663840
## 45.85714 4.585050 7.013369
## 46.00000 4.765379 7.288476
## 46.14286 4.973109 7.606825
## 46.28571 5.141237 7.863330
## 46.42857 5.332948 8.157126
## 46.57143 5.491261 8.398670
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 3.501310 3.420939 3.473305 3.618287 3.506480 3.506480 3.506480 3.506480
##  [9] 3.506480 3.506480 3.506480 3.506480 3.506480 3.506480
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##                 80%       95%
## 44.71429 -0.4923528 -2.606470
## 44.85714 -1.3175633 -3.825975
## 45.00000 -1.5150493 -4.155724
## 45.14286 -1.5057890 -4.218311
## 45.28571 -1.6650847 -4.402746
## 45.42857 -1.6783959 -4.423103
## 45.57143 -1.6916729 -4.443409
## 45.71429 -1.7049162 -4.463663
## 45.85714 -1.7181259 -4.483865
## 46.00000 -1.7313023 -4.504017
## 46.14286 -1.7444456 -4.524118
## 46.28571 -1.7575561 -4.544169
## 46.42857 -1.7706340 -4.564170
## 46.57143 -1.7836796 -4.584121
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 7.494973  9.60909
## 44.85714 8.159442 10.66785
## 45.00000 8.461659 11.10233
## 45.14286 8.742363 11.45488
## 45.28571 8.678045 11.41571
## 45.42857 8.691356 11.43606
## 45.57143 8.704633 11.45637
## 45.71429 8.717877 11.47662
## 45.85714 8.731086 11.49683
## 46.00000 8.744263 11.51698
## 46.14286 8.757406 11.53708
## 46.28571 8.770516 11.55713
## 46.42857 8.783594 11.57713
## 46.57143 8.796640 11.59708
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##  [1] 36.59558 37.85445 40.62936 37.27931 43.34780 37.45201 42.25492 40.49202
##  [9] 40.49202 40.49202 40.49202 40.49202 40.49202 40.49202
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%       95%
## 44.71429 24.75559 18.487884
## 44.85714 24.94917 18.117533
## 45.00000 26.74025 19.387807
## 45.14286 22.47160 14.632876
## 45.28571 27.67524 19.378683
## 45.42857 20.95989 12.229484
## 45.57143 24.98208 15.838384
## 45.71429 21.49967 11.445734
## 45.85714 20.47188  9.873866
## 46.00000 19.49434  8.378845
## 46.14286 18.56033  6.950395
## 46.28571 17.66450  5.580342
## 46.42857 16.80252  4.262061
## 46.57143 15.97083  2.990093
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 6) 
## End = c(46, 5) 
## Frequency = 7 
##               80%      95%
## 44.71429 48.43556 54.70327
## 44.85714 50.75973 57.59137
## 45.00000 54.51846 61.87091
## 45.14286 52.08702 59.92574
## 45.28571 59.02036 67.31691
## 45.42857 53.94413 62.67454
## 45.57143 59.52776 68.67146
## 45.71429 59.48436 69.53830
## 45.85714 60.51215 71.11017
## 46.00000 61.48969 72.60519
## 46.14286 62.42370 74.03364
## 46.28571 63.31953 75.40369
## 46.42857 64.18151 76.72197
## 46.57143 65.01321 77.99394